This document aims to provide further examples in how to use the hpgltools.
Note to self, the header has rmarkdown::pdf_document instead of html_document or html_vignette because it gets some bullcrap error ‘margins too large’…
Here are the commands I invoke to get ready to play with new data, including everything required to install hpgltools, the software it uses, and the fission data.
library(hpgltools)
tt <- sm(library(fission))
tt <- data(fission)
All the work I do in Dr. El-Sayed’s lab makes some pretty hard assumptions about how data is stored. As a result, to use the fission data set I will do a little bit of shenanigans to match it to the expected format. Now that I have played a little with fission, I think its format is quite nice and am likely to have my experiment class instead be a SummarizedExperiment.
## Extract the meta data from the fission dataset
meta <- as.data.frame(fission@colData)
## Make conditions and batches
meta$condition <- paste(meta$strain, meta$minute, sep=".")
meta$batch <- meta$replicate
meta$sample.id <- rownames(meta)
## Grab the count data
fission_data <- fission@assays$data$counts
## This will make an experiment superclass called 'expt' and it contains
## an ExpressionSet along with any arbitrary additional information one might want to include.
## Along the way it writes a Rdata file which is by default called 'expt.Rdata'
fission_expt <- create_expt(metadata=meta, count_dataframe=fission_data)
## Reading the sample metadata.
## The sample definitions comprises: 36, 7 rows, columns.
## Matched 7039 annotations and counts.
## Bringing together the count matrix and gene information.
Travis wisely imposes a limit on the amount of time for building vignettes. My tools by default will attempt all possible pairwise comparisons, which takes a long time. Therefore I am going to take a subset of the data and limit these comparisons to that.
fun_data <- subset_expt(fission_expt,
subset="condition=='wt.120'|condition=='wt.30'")
## There were 36, now there are 6 samples.
fun_norm <- sm(normalize_expt(fun_data, batch="limma", norm="quant",
transform="log2", convert="cpm"))
limma_comparison <- sm(limma_pairwise(fun_data))
names(limma_comparison$all_tables)
## [1] "wt30_vs_wt120"
summary(limma_comparison$all_tables$wt30_vs_wt120)
## logFC AveExpr t P.Value
## Min. :-4.278 Min. :-4.58 Min. :-88.48 Min. :0.0000
## 1st Qu.:-0.399 1st Qu.: 1.11 1st Qu.: -2.60 1st Qu.:0.0192
## Median :-0.020 Median : 3.97 Median : -0.13 Median :0.1240
## Mean : 0.008 Mean : 3.11 Mean : -0.17 Mean :0.2792
## 3rd Qu.: 0.300 3rd Qu.: 5.44 3rd Qu.: 1.72 3rd Qu.:0.4653
## Max. : 7.075 Max. :18.59 Max. : 62.44 Max. :1.0000
## adj.P.Val B
## Min. :0.0170 Min. :-8.29
## 1st Qu.:0.0767 1st Qu.:-6.58
## Median :0.2479 Median :-5.50
## Mean :0.3686 Mean :-4.87
## 3rd Qu.:0.6204 3rd Qu.:-3.50
## Max. :1.0000 Max. : 4.83
scatter_wt_mut <- extract_coefficient_scatter(limma_comparison, type="limma",
x="wt30", y="wt120")
## This can do comparisons among the following columns in the pairwise result:
## wt120, wt30
## Actually comparing wt30 and wt120.
scatter_wt_mut$scatter
scatter_wt_mut$both_histogram$plot + ggplot2::scale_y_continuous(limits=c(0, 0.20))
## Warning: Removed 7039 rows containing non-finite values (stat_bin).
## Warning: Removed 7039 rows containing non-finite values (stat_density).
## Warning: Removed 4 rows containing missing values (geom_bar).
## Warning: Removed 1 rows containing missing values (geom_vline).
ma_wt_mut <- extract_de_plots(limma_comparison, type="limma")
ma_wt_mut$ma$plot
ma_wt_mut$volcano$plot
deseq_comparison <- sm(deseq2_pairwise(fun_data))
summary(deseq_comparison$all_tables$wt30_vs_wt120)
## baseMean logFC lfcSE stat
## Min. : 0 Min. :-5.615 Min. :0.000 Min. :-20.800
## 1st Qu.: 28 1st Qu.:-0.386 1st Qu.:0.168 1st Qu.: -1.176
## Median : 192 Median : 0.000 Median :0.222 Median : 0.000
## Mean : 1703 Mean : 0.020 Mean :0.489 Mean : 0.168
## 3rd Qu.: 536 3rd Qu.: 0.343 3rd Qu.:0.412 3rd Qu.: 1.109
## Max. :4924000 Max. : 7.212 Max. :4.072 Max. : 30.370
## P.Value adj.P.Val
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0197 1st Qu.:0.0685
## Median :0.2503 Median :0.4676
## Mean :0.3600 Mean :0.4805
## 3rd Qu.:0.6666 3rd Qu.:0.8732
## Max. :1.0000 Max. :1.0000
scatter_wt_mut <- extract_coefficient_scatter(deseq_comparison, type="deseq",
x="wt30", y="wt120", gvis_filename=NULL)
## This can do comparisons among the following columns in the pairwise result:
## wt120, wt30, r2, r3
## Actually comparing wt30 and wt120.
scatter_wt_mut$scatter
plots_wt_mut <- extract_de_plots(deseq_comparison, type="deseq")
plots_wt_mut$ma$plot
plots_wt_mut$volcano$plot
edger_comparison <- sm(edger_pairwise(fun_data, model_batch=TRUE))
plots_wt_mut <- extract_de_plots(edger_comparison, type="edger")
scatter_wt_mut <- extract_coefficient_scatter(edger_comparison, type="edger",
x="wt30", y="wt120", gvis_filename=NULL)
## This can do comparisons among the following columns in the pairwise result:
## wt120, wt30
## Actually comparing wt30 and wt120.
scatter_wt_mut$scatter
plots_wt_mut$ma$plot
plots_wt_mut$volcano$plot
basic_comparison <- sm(basic_pairwise(fun_data))
summary(basic_comparison$all_tables$wt30_vs_wt120)
## numerator_median denominator_median numerator_var denominator_var
## Min. :-2.73 Min. :-3.60 Length:5505 Length:5505
## 1st Qu.: 3.31 1st Qu.: 3.31 Class :character Class :character
## Median : 4.65 Median : 4.63 Mode :character Mode :character
## Mean : 4.71 Mean : 4.71
## 3rd Qu.: 5.94 3rd Qu.: 5.93
## Max. :18.61 Max. :18.61
## t p logFC adjp
## Min. :-49.10 Length:5505 Min. :-4.263 Length:5505
## 1st Qu.: -1.53 Class :character 1st Qu.:-0.406 Class :character
## Median : 0.39 Mode :character Median :-0.070 Mode :character
## Mean : 0.16 Mean : 0.008
## 3rd Qu.: 2.10 3rd Qu.: 0.297
## Max. : 50.21 Max. : 7.485
scatter_wt_mut <- extract_coefficient_scatter(basic_comparison, type="basic",
x="wt30", y="wt120", gvis_filename=NULL)
## This can do comparisons among the following columns in the pairwise result:
## wt120, wt30
## Actually comparing wt30 and wt120.
scatter_wt_mut$scatter
plots_wt_mut <- extract_de_plots(basic_comparison, type="basic")
plots_wt_mut$ma$plot
plots_wt_mut$volcano$plot
all_comparisons <- sm(all_pairwise(fun_data, model_batch=TRUE))
all_combined <- sm(combine_de_tables(all_comparisons, excel=FALSE))
head(all_combined$data[[1]])
## limma_logfc limma_adjp deseq_logfc deseq_adjp edger_logfc
## SPAC1002.01 -0.99860 0.16930 -1.08000 0.36640 -1.05900
## SPAC1002.02 0.03778 0.99460 -0.01485 0.98160 -0.02342
## SPAC1002.03c -0.33910 0.02432 -0.22760 0.23270 -0.23630
## SPAC1002.04c 0.31760 0.33060 0.33550 0.32470 0.32580
## SPAC1002.05c 0.75440 0.08050 0.74810 0.01187 0.74120
## SPAC1002.06c 0.69490 0.68500 0.50550 1.00000 0.69240
## edger_adjp limma_ave limma_t limma_b limma_p deseq_basemean
## SPAC1002.01 0.2201000 -0.1955 -2.8320 -4.0790 0.07147 11.150
## SPAC1002.02 1.0000000 2.8470 0.1354 -7.4050 0.90140 87.420
## SPAC1002.03c 0.1598000 7.0770 -12.5400 -0.6495 0.00151 1621.000
## SPAC1002.04c 0.2151000 4.1960 1.7300 -6.5590 0.18830 222.200
## SPAC1002.05c 0.0009489 3.9020 4.6960 -3.7270 0.02118 187.200
## SPAC1002.06c 0.7328000 -1.9060 0.7146 -6.1430 0.52970 4.176
## deseq_lfcse deseq_stat deseq_p edger_logcpm edger_lr
## SPAC1002.01 0.8209 -1.31600 0.188200 0.06691 2.745000
## SPAC1002.02 0.3316 -0.04479 0.964300 2.89400 0.007429
## SPAC1002.03c 0.1387 -1.64100 0.100800 7.09500 3.399000
## SPAC1002.04c 0.2382 1.40800 0.159000 4.24700 2.794000
## SPAC1002.05c 0.2464 3.03700 0.002393 3.99900 14.270000
## SPAC1002.06c 1.4310 0.35330 0.723800 -0.89280 0.370800
## edger_p basic_nummed basic_denmed basic_numvar basic_denvar
## SPAC1002.01 0.097570 0.000 0.000 0 0
## SPAC1002.02 0.931300 3.100 2.774 3.603e-01 2.890e-02
## SPAC1002.03c 0.065220 6.909 7.248 2.955e-03 1.016e-03
## SPAC1002.04c 0.094620 4.407 4.195 5.418e-02 1.441e-01
## SPAC1002.05c 0.000158 4.272 3.625 1.293e-01 5.645e-02
## SPAC1002.06c 0.542600 0.000 0.000 0 0
## basic_logfc basic_t basic_p basic_adjp limma_adjp_fdr
## SPAC1002.01 0.0000 0.00000 0 0 1.693e-01
## SPAC1002.02 0.3260 -0.01124 9.919e-01 9.963e-01 9.945e-01
## SPAC1002.03c -0.3390 9.25600 1.969e-03 3.718e-02 2.432e-02
## SPAC1002.04c 0.2125 -1.26900 2.862e-01 4.542e-01 3.305e-01
## SPAC1002.05c 0.6472 -2.95900 4.975e-02 1.630e-01 8.050e-02
## SPAC1002.06c 0.0000 0.00000 0 0 6.850e-01
## deseq_adjp_fdr edger_adjp_fdr basic_adjp_fdr lfc_meta
## SPAC1002.01 4.186e-01 2.201e-01 0.000e+00 -1.0520000
## SPAC1002.02 1.000e+00 1.000e+00 9.954e-01 0.0007106
## SPAC1002.03c 2.682e-01 1.598e-01 7.607e-03 -0.2681000
## SPAC1002.04c 3.722e-01 2.151e-01 4.027e-01 0.3262000
## SPAC1002.05c 1.393e-02 9.489e-04 1.090e-01 0.7512000
## SPAC1002.06c 9.279e-01 7.328e-01 0.000e+00 0.6331000
## lfc_var lfc_varbymed p_meta p_var
## SPAC1002.01 7.689e-04 -7.305e-04 1.191e-01 3.753e-03
## SPAC1002.02 3.191e-03 4.491e+00 9.323e-01 9.899e-04
## SPAC1002.03c 2.166e-03 -8.081e-03 5.584e-02 2.531e-03
## SPAC1002.04c 1.943e-04 5.956e-04 1.473e-01 2.297e-03
## SPAC1002.05c 1.320e-03 1.757e-03 7.910e-03 1.333e-04
## SPAC1002.06c 1.516e-02 2.394e-02 5.987e-01 1.178e-02
sig_genes <- sm(extract_significant_genes(all_combined, excel=FALSE))
head(sig_genes$limma$ups[[1]])
## limma_logfc limma_adjp deseq_logfc deseq_adjp edger_logfc
## SPBC2F12.09c 7.075 0.01847 7.212 5.259e-66 7.170
## SPAC22A12.17c 5.609 0.02447 5.855 3.969e-19 5.822
## SPAPB1A11.02 5.606 0.01696 6.739 1.894e-06 6.483
## SPCPB16A4.07 5.576 0.01696 5.693 8.168e-199 5.684
## SPNCRNA.1611 5.410 0.01696 5.612 5.051e-16 5.529
## SPBC660.05 5.229 0.02795 5.403 3.101e-14 5.310
## edger_adjp limma_ave limma_t limma_b limma_p
## SPBC2F12.09c 1.264e-180 2.5920 19.36 0.8017 0.0004519
## SPAC22A12.17c 3.155e-57 6.4820 12.49 -0.3953 0.0015260
## SPAPB1A11.02 1.257e-14 -1.1920 27.66 0.2698 0.0001667
## SPCPB16A4.07 4.519e-138 6.5360 28.74 2.2890 0.0001498
## SPNCRNA.1611 1.789e-33 0.5594 39.34 1.0980 0.0000622
## SPBC660.05 9.868e-74 3.6400 10.56 -0.5550 0.0024270
## deseq_basemean deseq_lfcse deseq_stat deseq_p
## SPBC2F12.09c 443.50 0.4123 17.490 1.667e-68
## SPAC22A12.17c 4289.00 0.6255 9.360 7.945e-21
## SPAPB1A11.02 21.20 1.2810 5.259 1.447e-07
## SPCPB16A4.07 4157.00 0.1875 30.370 1.363e-202
## SPNCRNA.1611 58.57 0.6571 8.541 1.331e-17
## SPBC660.05 523.80 0.6724 8.035 9.365e-16
## edger_logcpm edger_lr edger_p basic_nummed basic_denmed
## SPBC2F12.09c 5.2210 839.00 1.796e-184 6.250 -1.235
## SPAC22A12.17c 8.4930 264.90 1.479e-59 9.396 4.087
## SPAPB1A11.02 0.9166 65.83 4.910e-16 1.491 -3.604
## SPCPB16A4.07 8.4490 641.90 1.284e-141 9.416 3.641
## SPNCRNA.1611 2.3170 154.00 2.363e-35 3.380 -1.604
## SPBC660.05 5.4560 341.60 2.804e-76 6.156 1.381
## basic_numvar basic_denvar basic_logfc basic_t basic_p
## SPBC2F12.09c 2.211e-02 5.043e-01 7.485 -16.760 2.432e-03
## SPAC22A12.17c 2.236e-02 9.694e-01 5.309 -10.080 8.325e-03
## SPAPB1A11.02 6.869e-01 4.981e-01 5.095 -7.708 1.687e-03
## SPCPB16A4.07 2.022e-02 2.894e-01 5.776 -17.480 1.780e-03
## SPNCRNA.1611 1.174e-01 1.141e-01 4.984 -18.270 5.286e-05
## SPBC660.05 2.068e-01 5.143e-01 4.775 -10.840 9.581e-04
## basic_adjp limma_adjp_fdr deseq_adjp_fdr edger_adjp_fdr
## SPBC2F12.09c 3.928e-02 1.847e-02 6.176e-66 1.264e-180
## SPAC22A12.17c 6.437e-02 2.447e-02 4.660e-19 3.155e-57
## SPAPB1A11.02 3.452e-02 1.696e-02 2.224e-06 1.257e-14
## SPCPB16A4.07 3.524e-02 1.696e-02 9.594e-199 4.519e-138
## SPNCRNA.1611 1.455e-02 1.696e-02 5.930e-16 1.789e-33
## SPBC660.05 2.791e-02 2.795e-02 3.642e-14 9.869e-74
## basic_adjp_fdr lfc_meta lfc_var lfc_varbymed p_meta
## SPBC2F12.09c 9.147e-03 7.152 0.000e+00 0.000e+00 1.506e-04
## SPAC22A12.17c 2.609e-02 5.916 1.123e-01 1.898e-02 5.087e-04
## SPAPB1A11.02 6.586e-03 6.137 5.880e-02 9.581e-03 5.561e-05
## SPCPB16A4.07 6.915e-03 5.632 2.810e-02 4.989e-03 4.993e-05
## SPNCRNA.1611 2.394e-04 5.521 2.657e-02 4.813e-03 2.073e-05
## SPBC660.05 3.914e-03 5.347 2.521e-02 4.714e-03 8.090e-04
## p_var
## SPBC2F12.09c 6.807e-08
## SPAC22A12.17c 7.762e-07
## SPAPB1A11.02 9.255e-09
## SPCPB16A4.07 7.480e-09
## SPNCRNA.1611 1.290e-09
## SPBC660.05 1.963e-06
## Here we see that edger and deseq agree the least:
all_comparisons$comparison$comp
## wt30_vs_wt120
## le 0.9601
## ld 0.9808
## ed 0.9814
## lb 0.9779
## eb 0.9782
## db 0.9777
## And here we can look at the set of 'significant' genes according to various tools:
yeast_sig <- extract_significant_genes(all_combined, excel=FALSE)
## Writing excel data for wt30_vs_wt120: 1/4.
## After (adj)p filter, the up genes table has 633 genes.
## After (adj)p filter, the down genes table has 629 genes.
## After fold change filter, the up genes table has 325 genes.
## After fold change filter, the down genes table has 201 genes.
## Writing excel data for wt30_vs_wt120: 2/4.
## After (adj)p filter, the up genes table has 1107 genes.
## After (adj)p filter, the down genes table has 1052 genes.
## After fold change filter, the up genes table has 447 genes.
## After fold change filter, the down genes table has 278 genes.
## Writing excel data for wt30_vs_wt120: 3/4.
## After (adj)p filter, the up genes table has 904 genes.
## After (adj)p filter, the down genes table has 733 genes.
## After fold change filter, the up genes table has 403 genes.
## After fold change filter, the down genes table has 219 genes.
## Writing excel data for wt30_vs_wt120: 4/4.
## After (adj)p filter, the up genes table has 277 genes.
## After (adj)p filter, the down genes table has 237 genes.
## After fold change filter, the up genes table has 185 genes.
## After fold change filter, the down genes table has 105 genes.
yeast_barplots <- sm(significant_barplots(combined=all_combined))
yeast_barplots$limma
yeast_barplots$edger
yeast_barplots$deseq
Since I didn’t acquire this data in a ‘normal’ way, I am going to post-generate a gff file which may be used by clusterprofiler, topgo, and gostats.
Therefore, I am going to make use of TxDb to make the requisite gff file.
limma_results <- limma_comparison$all_tables
## The set of comparisons performed
names(limma_results)
## [1] "wt30_vs_wt120"
table <- limma_results$wt30_vs_wt120
dim(table)
## [1] 7039 6
gene_names <- rownames(table)
updown_genes <- get_sig_genes(table, p=0.05, lfc=0.4, p_column="P.Value")
## After (adj)p filter, the up genes table has 1190 genes.
## After (adj)p filter, the down genes table has 1424 genes.
## After fold change filter, the up genes table has 962 genes.
## After fold change filter, the down genes table has 1069 genes.
tt <- please_install("GenomicFeatures")
tt <- please_install("biomaRt")
available_marts <- biomaRt::listMarts(host="fungi.ensembl.org")
available_marts
## biomart version
## 1 fungi_mart Ensembl Fungi Genes 39
## 2 fungi_variations Ensembl Fungi Variations 39
ensembl_mart <- biomaRt::useMart("fungi_mart", host="fungi.ensembl.org")
available_datasets <- biomaRt::listDatasets(ensembl_mart)
pombe_hit <- grep(pattern="pombe", x=available_datasets[["description"]])
pombe_name <- available_datasets[pombe_hit, "dataset"]
pombe_mart <- biomaRt::useDataset(pombe_name, mart=ensembl_mart)
pombe_goids <- biomaRt::getBM(attributes=c("pombase_transcript", "go_id"),
values=gene_names, mart=pombe_mart)
colnames(pombe_goids) <- c("ID", "GO")
The above worked, it provided a table of ID and ontology. It was however a bit fraught. Here is another way.
## In theory, the above should work with a single function call:
pombe_goids_simple <- load_biomart_go(species="spombe", overwrite=TRUE,
dl_rows=c("pombase_transcript", "go_id"),
host="fungi.ensembl.org")
## Unable to perform useMart, perhaps the host/mart is incorrect: fungi.ensembl.org ENSEMBL_MART_ENSEMBL.
## The available marts are:
## fungi_martfungi_variations
## Trying the first one.
## Unable to perform useDataset, perhaps the given dataset is incorrect: spombe_gene_ensembl.
## Trying instead to use the dataset: spombe_eg_gene
## That seems to have worked, extracting the resulting annotations.
## Finished downloading ensembl go annotations, saving to spombe_go_annotations.rda.
## Saving ontologies to spombe_go_annotations.rda.
## Finished save().
head(pombe_goids_simple)
## ID GO
## 1 SPRRNA.50.1
## 2 SPNCRNA.1095.1
## 3 SPAC212.11.1 GO:0000784
## 4 SPAC212.11.1 GO:0005634
## 5 SPAC212.11.1 GO:0000166
## 6 SPAC212.11.1 GO:0005524
head(pombe_goids)
## ID GO
## 1 SPRRNA.50.1
## 2 SPNCRNA.1095.1
## 3 SPAC212.11.1 GO:0000784
## 4 SPAC212.11.1 GO:0005634
## 5 SPAC212.11.1 GO:0000166
## 6 SPAC212.11.1 GO:0005524
## This used to work, but does so no longer and I do not know why.
## pombe <- sm(GenomicFeatures::makeTxDbFromBiomart(biomart="fungal_mart",
## dataset="spombe_eg_gene",
## host="fungi.ensembl.org"))
## I bet I can get all this information from ensembl now.
## This was found at the bottom of: https://www.biostars.org/p/232005/
link <- "ftp://ftp.ensemblgenomes.org/pub/release-34/fungi/gff3/schizosaccharomyces_pombe/Schizosaccharomyces_pombe.ASM294v2.34.gff3.gz"
pombe <- GenomicFeatures::makeTxDbFromGFF(link, format="gff3", organism="Schizosaccharomyces pombe",
taxonomyId=4896)
## Import genomic features from the file as a GRanges object ...
## OK
## Prepare the 'metadata' data frame ... OK
## Make the TxDb object ... OK
pombe_transcripts <- as.data.frame(GenomicFeatures::transcriptsBy(pombe))
lengths <- pombe_transcripts[, c("group_name","width")]
colnames(lengths) <- c("ID","width")
## Something useful I didn't notice before:
## makeTranscriptDbFromGFF() ## From GenomicFeatures, much like my own gff2df()
gff_from_txdb <- GenomicFeatures::asGFF(pombe)
## why is GeneID: getting prefixed to the IDs!?
gff_from_txdb$ID <- gsub(x=gff_from_txdb$ID, pattern="GeneID:", replacement="")
written_gff <- rtracklayer::export.gff3(gff_from_txdb, con="pombe.gff")
summary(updown_genes)
## Length Class Mode
## up_genes 6 data.frame list
## down_genes 6 data.frame list
test_genes <- updown_genes$down_genes
rownames(test_genes) <- paste0(rownames(test_genes), ".1")
lengths$ID <- paste0(lengths$ID, ".1")
goseq_result <- sm(simple_goseq(sig_genes=test_genes, go_db=pombe_goids, length_db=lengths))
head(goseq_result$alldata)
## category over_represented_pvalue under_represented_pvalue
## 338 GO:0005634 5.646e-44 1
## 347 GO:0005730 8.262e-34 1
## 1208 GO:0042254 1.198e-29 1
## 126 GO:0003674 1.972e-27 1
## 349 GO:0005737 1.157e-24 1
## 380 GO:0005829 6.801e-21 1
## numDEInCat numInCat term ontology qvalue
## 338 100 579 nucleus CC 9.282e-41
## 347 35 61 nucleolus CC 6.792e-31
## 1208 26 36 ribosome biogenesis BP 6.567e-27
## 126 76 529 molecular_function MF 8.106e-25
## 349 77 574 cytoplasm CC 3.804e-22
## 380 70 550 cytosol CC 1.863e-18
goseq_result$pvalue_plots$mfp_plot
test_genes <- updown_genes$up_genes
rownames(test_genes) <- paste0(rownames(test_genes), ".1")
goseq_result <- sm(simple_goseq(sig_genes=test_genes, go_db=pombe_goids, length_db=lengths))
head(goseq_result$alldata)
## category over_represented_pvalue under_represented_pvalue numDEInCat
## 640 GO:0008150 1.540e-55 1 127
## 380 GO:0005829 1.587e-50 1 124
## 349 GO:0005737 7.741e-50 1 126
## 338 GO:0005634 1.380e-44 1 120
## 126 GO:0003674 3.390e-44 1 113
## 854 GO:0016020 2.814e-39 1 96
## numInCat term ontology qvalue
## 640 544 biological_process BP 2.532e-52
## 380 550 cytosol CC 1.304e-47
## 349 574 cytoplasm CC 4.242e-47
## 338 579 nucleus CC 5.674e-42
## 126 529 molecular_function MF 1.114e-41
## 854 411 membrane CC 7.712e-37
goseq_result$pvalue_plots$bpp_plot
clusterProfiler really prefers an orgdb instance to use, which is probably smart, as they are pretty nice. Sadly, there is no pre-defined orgdb for pombe…
## holy crap makeOrgPackageFromNCBI is slow, no slower than some of mine, so who am I to complain.
orgdb <- AnnotationForge::makeOrgPackageFromNCBI(version="0.1", author="atb <abelew@gmail.com>",
maintainer="atb <abelew@gmail.com>", tax_id="4896",
genus="Schizosaccharomyces", species="pombe")
## This created the directory 'org.spombe.eg.db'
devtools::install_local("org.Spombe.eg.db")
library(org.Spombe.eg.db)
## Don't forget to remove the terminal .1 from the gene names...
## If you do forget this, it will fail for no easily visible reason until you remember
## this and get really mad at yourself.
rownames(test_genes) <- gsub(pattern=".1$", replacement="", x=rownames(test_genes))
pombe_goids[["ID"]] <- gsub(pattern=".1$", replacement="", x=pombe_goids[["ID"]])
cp_result <- simple_clusterprofiler(sig_genes=test_genes, do_david=FALSE, do_gsea=FALSE,
de_table=all_combined$data[[1]],
orgdb=org.Spombe.eg.db, orgdb_to="ALIAS")
cp_result[["pvalue_plots"]][["ego_all_mf"]]
## Yay bar plots!
## Get rid of those stupid terminal .1s.
rownames(test_genes) <- gsub(pattern=".1$", replacement="", x=rownames(test_genes))
pombe_goids[["ID"]] <- gsub(pattern=".1$", replacement="", x=pombe_goids[["ID"]])
tp_result <- sm(simple_topgo(sig_genes=test_genes, go_db=pombe_goids, pval_column="limma_adjp"))
tp_result[["pvalue_plots"]][["mfp_plot_over"]]
tp_result[["pvalue_plots"]][["bpp_plot_over"]]
## Get rid of those stupid terminal .1s.
rownames(test_genes) <- gsub(pattern=".1$", replacement="", x=rownames(test_genes))
pombe_goids[["ID"]] <- gsub(pattern=".1$", replacement="", x=pombe_goids[["ID"]])
## universe_merge is the column in the final data frame when.
## gff_type is the field in the gff file providing the id, this may be redundant with
## universe merge, that is something to check on...
gst_result <- sm(simple_gostats(sig_genes=test_genes, go_db=pombe_goids, universe_merge="id",
gff_type="gene",
gff="pombe.gff", pval_column="limma_adjp"))
pander::pander(sessionInfo())
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
locale: LC_CTYPE=en_US.utf8, LC_NUMERIC=C, LC_TIME=en_US.utf8, LC_COLLATE=en_US.utf8, LC_MONETARY=en_US.utf8, LC_MESSAGES=en_US.utf8, LC_PAPER=en_US.utf8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.utf8 and LC_IDENTIFICATION=C
attached base packages: parallel, stats4, stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: edgeR(v.3.22.2), variancePartition(v.1.10.0), ggplot2(v.2.2.1), fission(v.0.114.0), SummarizedExperiment(v.1.10.1), DelayedArray(v.0.6.0), BiocParallel(v.1.14.1), matrixStats(v.0.53.1), Biobase(v.2.40.0), GenomicRanges(v.1.32.3), GenomeInfoDb(v.1.16.0), IRanges(v.2.14.10), S4Vectors(v.0.18.2), BiocGenerics(v.0.26.0), foreach(v.1.4.4), Vennerable(v.3.1.0.9000), ruv(v.0.9.7) and hpgltools(v.2018.03)
loaded via a namespace (and not attached): snow(v.0.4-2), backports(v.1.1.2), Hmisc(v.4.1-1), plyr(v.1.8.4), lazyeval(v.0.2.1), splines(v.3.5.1), sva(v.3.28.0), digest(v.0.6.15), BiocInstaller(v.1.30.0), htmltools(v.0.3.6), GO.db(v.3.6.0), gdata(v.2.18.0), magrittr(v.1.5), checkmate(v.1.8.5), memoise(v.1.1.0), cluster(v.2.0.7-1), doParallel(v.1.0.11), openxlsx(v.4.1.0), limma(v.3.36.1), Biostrings(v.2.48.0), annotate(v.1.58.0), prettyunits(v.1.0.2), colorspace(v.1.3-2), blob(v.1.1.1), ggrepel(v.0.8.0), BiasedUrn(v.1.07), RCurl(v.1.95-4.10), graph(v.1.58.0), genefilter(v.1.62.0), lme4(v.1.1-17), survival(v.2.42-4), iterators(v.1.0.9), gtable(v.0.2.0), zlibbioc(v.1.26.0), XVector(v.0.20.0), DEoptimR(v.1.0-8), SparseM(v.1.77), scales(v.0.5.0), DESeq(v.1.32.0), DBI(v.1.0.0), Rcpp(v.0.12.17), genoPlotR(v.0.8.7), xtable(v.1.8-2), progress(v.1.1.2), htmlTable(v.1.12), foreign(v.0.8-70), bit(v.1.1-14), preprocessCore(v.1.42.0), Formula(v.1.2-3), htmlwidgets(v.1.2), httr(v.1.3.1), gplots(v.3.0.1), RColorBrewer(v.1.1-2), acepack(v.1.4.1), pkgconfig(v.2.0.1), XML(v.3.98-1.11), nnet(v.7.3-12), locfit(v.1.5-9.1), labeling(v.0.3), rlang(v.0.2.1), reshape2(v.1.4.3), AnnotationDbi(v.1.42.1), munsell(v.0.4.3), tools(v.3.5.1), RSQLite(v.2.1.1), ade4(v.1.7-11), devtools(v.1.13.5), evaluate(v.0.10.1), stringr(v.1.3.1), yaml(v.2.1.19), knitr(v.1.20), bit64(v.0.9-7), geneLenDataBase(v.1.16.0), zip(v.1.0.0), pander(v.0.6.1), robustbase(v.0.93-0), caTools(v.1.17.1), packrat(v.0.4.9-3), RBGL(v.1.56.0), nlme(v.3.1-137), biomaRt(v.2.36.1), compiler(v.3.5.1), pbkrtest(v.0.4-7), rstudioapi(v.0.7), curl(v.3.2), tibble(v.1.4.2), geneplotter(v.1.58.0), stringi(v.1.2.2), highr(v.0.6), GenomicFeatures(v.1.32.0), lattice(v.0.20-35), Matrix(v.1.2-14), nloptr(v.1.0.4), pillar(v.1.2.3), goseq(v.1.32.0), data.table(v.1.11.4), bitops(v.1.0-6), corpcor(v.1.6.9), qvalue(v.2.12.0), rtracklayer(v.1.40.3), colorRamps(v.2.3), R6(v.2.2.2), latticeExtra(v.0.6-28), directlabels(v.2018.05.22), topGO(v.2.32.0), KernSmooth(v.2.23-15), gridExtra(v.2.3), codetools(v.0.2-15), MASS(v.7.3-50), gtools(v.3.5.0), assertthat(v.0.2.0), DESeq2(v.1.20.0), rprojroot(v.1.3-2), withr(v.2.1.2), GenomicAlignments(v.1.16.0), Rsamtools(v.1.32.0), GenomeInfoDbData(v.1.1.0), mgcv(v.1.8-24), doSNOW(v.1.0.16), quadprog(v.1.5-5), grid(v.3.5.1), rpart(v.4.1-13), minqa(v.1.2.4), rmarkdown(v.1.9), Rtsne(v.0.13) and base64enc(v.0.1-3)